Analysis of Mood Changes and Facial Expressions during Cognitive Behavior Therapy through a Virtual Agent
Analysis of Mood Changes and Facial Expressions during Cognitive Behavior Therapy through a Virtual Agent
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通过虚拟代理分析认知行为治疗期间的情绪变化和面部表情
DOI：
10.1145/3395035.3425223
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Satoshi Nakamura
中科院分区：
文献类型：
--
作者：
Kazuhiro Shidara;Hiroki Tanaka;Hiroyoshi Adachi;D. Kanayama;Yukako Sakagami;Takashi Kudo;Satoshi Nakamura
在使用虚拟代理的认知行为疗法（CBT）中，面部表情处理预计对对话反应选择移情对话有用。不幸的是，它在当前作品中的使用仍然有限。造成这种情况的原因之一是缺乏通过面向 CBT 的交互来研究情绪变化面部表情之间的关系。这项研究证实，通过与虚拟代理互动并识别与情绪变化相关的面部表情，可以改善负面情绪。基于CBT的认知重构，我们创建了固定的对话场景并在虚拟代理中实现。我们记录了与 23 名本科生和研究生对话时的面部表情，计算了 17 种动作单位（AU），即面部动作的单位，并使用情绪分数的变化率和 AU 的变化量进行了相关性分析。平均情绪改善率为 35%，情绪改善与 AU5 (r = -0.51)、AU17 (r = 0.45)、AU25 (r = -0.43) 和 AU45 (r = 0.45) 相关。这些结果意味着情绪的变化反映在面部表情上。本研究中确定的 AU 有可能用于代理交互建模。
In cognitive behavior therapy (CBT) with a virtual agent, facial expression processing is expected to be useful for dialogue response selection empathic dialogue. Unfortunately, its use in current works remains limited. One reason for this situation is the lack of research on the relationship between mood changes facial expressions through CBT-oriented interaction. This study confirms the improvement of negative moods through interaction with a virtual agent and identifying facial expressions that correlate with mood changes. Based on the cognitive restructuring of CBT, we created a fixed dialogue scenario and implemented it in a virtual agent. We recorded facial expressions during dialogues with 23 undergraduate and graduate students, calculated 17 types of action units (AUs), which are the units of facial movements, and performed a correlation analysis using the change rate of mood scores and the amount of the changes in the AUs. The mean mood improvement rate was 35%, and the mood improvements showed correlations with AU5 (r = -0.51), AU17 (r = 0.45), AU25 (r = -0.43), and AU45 (r = 0.45). These results imply that mood changes are reflected in facial expressions. The AUs identified in this study have the potential to be used for agent-interaction modeling.
自闭症谱系障碍患者多模式自动化社交技能培训
影响因子：
3.7
作者：
Hiroki Tanaka;Hideki Negoro;Hidemi Iwasaka;Satoshi Nakamura
通讯作者：
Satoshi Nakamura